Comparing Dataset A and Dataset B

Dataset statistics

	Dataset A	Dataset B
Number of variables	12	12
Number of observations	446	446
Missing cells	443	437
Missing cells (%)	8.3%	8.2%
Duplicate rows	0	0
Duplicate rows (%)	0.0%	0.0%
Total size in memory	45.3 KiB	45.3 KiB
Average record size in memory	104.0 B	104.0 B

Variable types

	Dataset A	Dataset B
Numeric	5	5
Categorical	4	4
Text	3	3

Alerts

Dataset A	Dataset B
`Age` has 91 (20.4%) missing values	`Age` has 91 (20.4%) missing values	Missing
`Cabin` has 351 (78.7%) missing values	`Cabin` has 345 (77.4%) missing values	Missing
`PassengerId` has unique values	`PassengerId` has unique values	Unique
`Name` has unique values	`Name` has unique values	Unique
`SibSp` has 297 (66.6%) zeros	`SibSp` has 302 (67.7%) zeros	Zeros
`Parch` has 345 (77.4%) zeros	`Parch` has 338 (75.8%) zeros	Zeros
`Fare` has 12 (2.7%) zeros	Alert not present in this dataset	Zeros

Reproduction

	Dataset A	Dataset B
Analysis started	2023-12-05 15:50:46.515326	2023-12-05 15:50:50.896896
Analysis finished	2023-12-05 15:50:50.895633	2023-12-05 15:50:54.741189
Duration	4.38 seconds	3.84 seconds
Software version	ydata-profiling v0.0.dev0	ydata-profiling v0.0.dev0
Download configuration	config.json	config.json

PassengerId
Real number (ℝ)

	Dataset A	Dataset B
Distinct	446	446
Distinct (%)	100.0%	100.0%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	432.40135	448.72422

	Dataset A	Dataset B
Minimum	1	6
Maximum	889	891
Zeros	0	0
Zeros (%)	0.0%	0.0%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	1	6
5-th percentile	37.25	52.25
Q1	203.75	229.5
median	432.5	441.5
Q3	666.75	673.5
95-th percentile	832.5	849.75
Maximum	889	891
Range	888	885
Interquartile range (IQR)	463	444

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	259.54475	257.57268
Coefficient of variation (CV)	0.60024038	0.57401111
Kurtosis	-1.243708	-1.2069199
Mean	432.40135	448.72422
Median Absolute Deviation (MAD)	232.5	224.5
Skewness	0.010447404	0.024528039
Sum	192851	200131
Variance	67363.477	66343.688
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
512	1	0.2%
245	1	0.2%
399	1	0.2%
753	1	0.2%
150	1	0.2%
547	1	0.2%
503	1	0.2%
475	1	0.2%
109	1	0.2%
517	1	0.2%
Other values (436)	436	97.8%

Value	Count	Frequency (%)
499	1	0.2%
693	1	0.2%
758	1	0.2%
195	1	0.2%
72	1	0.2%
542	1	0.2%
306	1	0.2%
132	1	0.2%
715	1	0.2%
593	1	0.2%
Other values (436)	436	97.8%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	1	0.2%
2	1	0.2%
3	1	0.2%
4	1	0.2%
5	1	0.2%
6	1	0.2%
9	1	0.2%
11	1	0.2%
15	1	0.2%
17	1	0.2%

Value	Count	Frequency (%)
6	1	0.2%
9	1	0.2%
10	1	0.2%
12	1	0.2%
13	1	0.2%
14	1	0.2%
16	1	0.2%
17	1	0.2%
20	1	0.2%
22	1	0.2%

Value	Count	Frequency (%)
6	1	0.2%
9	1	0.2%
10	1	0.2%
12	1	0.2%
13	1	0.2%
14	1	0.2%
16	1	0.2%
17	1	0.2%
20	1	0.2%
22	1	0.2%

Value	Count	Frequency (%)
1	1	0.2%
2	1	0.2%
3	1	0.2%
4	1	0.2%
5	1	0.2%
6	1	0.2%
9	1	0.2%
11	1	0.2%
15	1	0.2%
17	1	0.2%

Survived
Categorical

	Dataset A	Dataset B
Distinct	2	2
Distinct (%)	0.4%	0.4%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

0	272
1	174

0	278
1	168

Length

	Dataset A	Dataset B
Max length	1	1
Median length	1	1
Mean length	1	1
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	446	446
Distinct characters	2	2
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	0	0
2nd row	0	0
3rd row	0	0
4th row	1	0
5th row	1	0

Common Values

Value	Count	Frequency (%)
0	272	61.0%
1	174	39.0%

Value	Count	Frequency (%)
0	278	62.3%
1	168	37.7%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
0	272	61.0%
1	174	39.0%

Value	Count	Frequency (%)
0	278	62.3%
1	168	37.7%

Most occurring characters

Value	Count	Frequency (%)
0	272	61.0%
1	174	39.0%

Value	Count	Frequency (%)
0	278	62.3%
1	168	37.7%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	446	100.0%

Value	Count	Frequency (%)
Decimal Number	446	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	272	61.0%
1	174	39.0%

Value	Count	Frequency (%)
0	278	62.3%
1	168	37.7%

Most occurring scripts

Value	Count	Frequency (%)
Common	446	100.0%

Value	Count	Frequency (%)
Common	446	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	272	61.0%
1	174	39.0%

Value	Count	Frequency (%)
0	278	62.3%
1	168	37.7%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	446	100.0%

Value	Count	Frequency (%)
ASCII	446	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	272	61.0%
1	174	39.0%

Value	Count	Frequency (%)
0	278	62.3%
1	168	37.7%

Pclass
Categorical

	Dataset A	Dataset B
Distinct	3	3
Distinct (%)	0.7%	0.7%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

3	263
1	100
2	83

3	232
1	112
2	102

Length

	Dataset A	Dataset B
Max length	1	1
Median length	1	1
Mean length	1	1
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	446	446
Distinct characters	3	3
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	3	1
2nd row	3	3
3rd row	3	3
4th row	3	3
5th row	3	3

Common Values

Value	Count	Frequency (%)
3	263	59.0%
1	100	22.4%
2	83	18.6%

Value	Count	Frequency (%)
3	232	52.0%
1	112	25.1%
2	102	22.9%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
3	263	59.0%
1	100	22.4%
2	83	18.6%

Value	Count	Frequency (%)
3	232	52.0%
1	112	25.1%
2	102	22.9%

Most occurring characters

Value	Count	Frequency (%)
3	263	59.0%
1	100	22.4%
2	83	18.6%

Value	Count	Frequency (%)
3	232	52.0%
1	112	25.1%
2	102	22.9%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	446	100.0%

Value	Count	Frequency (%)
Decimal Number	446	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
3	263	59.0%
1	100	22.4%
2	83	18.6%

Value	Count	Frequency (%)
3	232	52.0%
1	112	25.1%
2	102	22.9%

Most occurring scripts

Value	Count	Frequency (%)
Common	446	100.0%

Value	Count	Frequency (%)
Common	446	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
3	263	59.0%
1	100	22.4%
2	83	18.6%

Value	Count	Frequency (%)
3	232	52.0%
1	112	25.1%
2	102	22.9%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	446	100.0%

Value	Count	Frequency (%)
ASCII	446	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
3	263	59.0%
1	100	22.4%
2	83	18.6%

Value	Count	Frequency (%)
3	232	52.0%
1	112	25.1%
2	102	22.9%

Name
['Text', 'Text']

	Dataset A	Dataset B
Distinct	446	446
Distinct (%)	100.0%	100.0%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Length

	Dataset A	Dataset B
Max length	82	82
Median length	52	49
Mean length	27.426009	26.746637
Min length	12	12

Characters and Unicode

	Dataset A	Dataset B
Total characters	12232	11929
Distinct characters	60	59
Distinct categories	7	7 ?
Distinct scripts	2	2 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	446	446 ?
Unique (%)	100.0%	100.0%

Sample

	Dataset A	Dataset B
1st row	Webber, Mr. James	Allison, Mrs. Hudson J C (Bessie Waldo Daniels)
2nd row	Harknett, Miss. Alice Phoebe	Balkic, Mr. Cerin
3rd row	Andersson, Miss. Sigrid Elisabeth	Boulos, Miss. Nourelain
4th row	Dean, Master. Bertram Vere	Salonen, Mr. Johan Werner
5th row	Dahl, Mr. Karl Edwart	O'Connell, Mr. Patrick D

Value	Count	Frequency (%)
mr	259	14.0%
miss	94	5.1%
mrs	69	3.7%
william	32	1.7%
john	25	1.4%
henry	18	1.0%
thomas	16	0.9%
master	15	0.8%
johan	10	0.5%
frederick	10	0.5%
Other values (892)	1296	70.3%

Value	Count	Frequency (%)
mr	266	14.8%
miss	92	5.1%
mrs	60	3.3%
william	31	1.7%
john	22	1.2%
master	18	1.0%
henry	18	1.0%
charles	17	0.9%
james	15	0.8%
george	13	0.7%
Other values (870)	1246	69.3%

Most occurring characters

Value	Count	Frequency (%)
	1399	11.4%
r	986	8.1%
e	878	7.2%
a	853	7.0%
s	670	5.5%
n	652	5.3%
i	643	5.3%
M	570	4.7%
l	541	4.4%
o	508	4.2%
Other values (50)	4532	37.1%

Value	Count	Frequency (%)
	1353	11.3%
r	995	8.3%
e	885	7.4%
a	807	6.8%
n	663	5.6%
s	645	5.4%
i	641	5.4%
M	551	4.6%
l	517	4.3%
o	496	4.2%
Other values (49)	4376	36.7%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	7854	64.2%
Uppercase Letter	1858	15.2%
Space Separator	1399	11.4%
Other Punctuation	966	7.9%
Open Punctuation	73	0.6%
Close Punctuation	73	0.6%
Dash Punctuation	9	0.1%

Value	Count	Frequency (%)
Lowercase Letter	7689	64.5%
Uppercase Letter	1811	15.2%
Space Separator	1353	11.3%
Other Punctuation	944	7.9%
Close Punctuation	63	0.5%
Open Punctuation	63	0.5%
Dash Punctuation	6	0.1%

Most frequent character per category

Space Separator

Value	Count	Frequency (%)
	1399	100.0%

Value	Count	Frequency (%)
	1353	100.0%

Lowercase Letter

Value	Count	Frequency (%)
r	986	12.6%
e	878	11.2%
a	853	10.9%
s	670	8.5%
n	652	8.3%
i	643	8.2%
l	541	6.9%
o	508	6.5%
t	352	4.5%
h	273	3.5%
Other values (16)	1498	19.1%

Value	Count	Frequency (%)
r	995	12.9%
e	885	11.5%
a	807	10.5%
n	663	8.6%
s	645	8.4%
i	641	8.3%
l	517	6.7%
o	496	6.5%
t	343	4.5%
h	274	3.6%
Other values (16)	1423	18.5%

Uppercase Letter

Value	Count	Frequency (%)
M	570	30.7%
A	137	7.4%
J	117	6.3%
H	95	5.1%
S	91	4.9%
E	86	4.6%
B	81	4.4%
C	80	4.3%
W	67	3.6%
P	61	3.3%
Other values (15)	473	25.5%

Value	Count	Frequency (%)
M	551	30.4%
A	116	6.4%
H	110	6.1%
J	97	5.4%
C	92	5.1%
E	90	5.0%
S	83	4.6%
R	71	3.9%
B	69	3.8%
L	68	3.8%
Other values (14)	464	25.6%

Other Punctuation

Value	Count	Frequency (%)
.	447	46.3%
,	446	46.2%
"	64	6.6%
'	8	0.8%
/	1	0.1%

Value	Count	Frequency (%)
.	446	47.2%
,	446	47.2%
"	46	4.9%
'	5	0.5%
/	1	0.1%

Open Punctuation

Value	Count	Frequency (%)
(	73	100.0%

Value	Count	Frequency (%)
(	63	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	73	100.0%

Value	Count	Frequency (%)
)	63	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	9	100.0%

Value	Count	Frequency (%)
-	6	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	9712	79.4%
Common	2520	20.6%

Value	Count	Frequency (%)
Latin	9500	79.6%
Common	2429	20.4%

Most frequent character per script

Common

Value	Count	Frequency (%)
	1399	55.5%
.	447	17.7%
,	446	17.7%
(	73	2.9%
)	73	2.9%
"	64	2.5%
-	9	0.4%
'	8	0.3%
/	1	< 0.1%

Value	Count	Frequency (%)
	1353	55.7%
.	446	18.4%
,	446	18.4%
)	63	2.6%
(	63	2.6%
"	46	1.9%
-	6	0.2%
'	5	0.2%
/	1	< 0.1%

Latin

Value	Count	Frequency (%)
r	986	10.2%
e	878	9.0%
a	853	8.8%
s	670	6.9%
n	652	6.7%
i	643	6.6%
M	570	5.9%
l	541	5.6%
o	508	5.2%
t	352	3.6%
Other values (41)	3059	31.5%

Value	Count	Frequency (%)
r	995	10.5%
e	885	9.3%
a	807	8.5%
n	663	7.0%
s	645	6.8%
i	641	6.7%
M	551	5.8%
l	517	5.4%
o	496	5.2%
t	343	3.6%
Other values (40)	2957	31.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	12232	100.0%

Value	Count	Frequency (%)
ASCII	11929	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	1399	11.4%
r	986	8.1%
e	878	7.2%
a	853	7.0%
s	670	5.5%
n	652	5.3%
i	643	5.3%
M	570	4.7%
l	541	4.4%
o	508	4.2%
Other values (50)	4532	37.1%

Value	Count	Frequency (%)
	1353	11.3%
r	995	8.3%
e	885	7.4%
a	807	6.8%
n	663	5.6%
s	645	5.4%
i	641	5.4%
M	551	4.6%
l	517	4.3%
o	496	4.2%
Other values (49)	4376	36.7%

Sex
Categorical

	Dataset A	Dataset B
Distinct	2	2
Distinct (%)	0.4%	0.4%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

male	284
female	162

male	293
female	153

Length

	Dataset A	Dataset B
Max length	6	6
Median length	4	4
Mean length	4.7264574	4.6860987
Min length	4	4

Characters and Unicode

	Dataset A	Dataset B
Total characters	2108	2090
Distinct characters	5	5
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	male	female
2nd row	female	male
3rd row	female	female
4th row	male	male
5th row	male	male

Common Values

Value	Count	Frequency (%)
male	284	63.7%
female	162	36.3%

Value	Count	Frequency (%)
male	293	65.7%
female	153	34.3%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
male	284	63.7%
female	162	36.3%

Value	Count	Frequency (%)
male	293	65.7%
female	153	34.3%

Most occurring characters

Value	Count	Frequency (%)
e	608	28.8%
m	446	21.2%
a	446	21.2%
l	446	21.2%
f	162	7.7%

Value	Count	Frequency (%)
e	599	28.7%
m	446	21.3%
a	446	21.3%
l	446	21.3%
f	153	7.3%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	2108	100.0%

Value	Count	Frequency (%)
Lowercase Letter	2090	100.0%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	608	28.8%
m	446	21.2%
a	446	21.2%
l	446	21.2%
f	162	7.7%

Value	Count	Frequency (%)
e	599	28.7%
m	446	21.3%
a	446	21.3%
l	446	21.3%
f	153	7.3%

Most occurring scripts

Value	Count	Frequency (%)
Latin	2108	100.0%

Value	Count	Frequency (%)
Latin	2090	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	608	28.8%
m	446	21.2%
a	446	21.2%
l	446	21.2%
f	162	7.7%

Value	Count	Frequency (%)
e	599	28.7%
m	446	21.3%
a	446	21.3%
l	446	21.3%
f	153	7.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	2108	100.0%

Value	Count	Frequency (%)
ASCII	2090	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	608	28.8%
m	446	21.2%
a	446	21.2%
l	446	21.2%
f	162	7.7%

Value	Count	Frequency (%)
e	599	28.7%
m	446	21.3%
a	446	21.3%
l	446	21.3%
f	153	7.3%

Age
Real number (ℝ)

	Dataset A	Dataset B
Distinct	72	76
Distinct (%)	20.3%	21.4%
Missing	91	91
Missing (%)	20.4%	20.4%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	29.284986	29.341324

	Dataset A	Dataset B
Minimum	0.42	0.42
Maximum	74	71
Zeros	0	0
Zeros (%)	0.0%	0.0%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0.42	0.42
5-th percentile	4.7	3.7
Q1	19.5	20
median	28	28
Q3	37.5	39
95-th percentile	54	54
Maximum	74	71
Range	73.58	70.58
Interquartile range (IQR)	18	19

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	14.16599	14.453535
Coefficient of variation (CV)	0.48372876	0.49259997
Kurtosis	0.15303096	-0.24400782
Mean	29.284986	29.341324
Median Absolute Deviation (MAD)	9	9
Skewness	0.40395706	0.21125129
Sum	10396.17	10416.17
Variance	200.67527	208.90468
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
19	16	3.6%
24	15	3.4%
36	14	3.1%
28	14	3.1%
16	12	2.7%
22	12	2.7%
18	12	2.7%
25	12	2.7%
26	12	2.7%
32	11	2.5%
Other values (62)	225	50.4%
(Missing)	91	20.4%

Value	Count	Frequency (%)
25	13	2.9%
21	13	2.9%
24	13	2.9%
18	12	2.7%
30	12	2.7%
22	12	2.7%
19	12	2.7%
20	12	2.7%
16	11	2.5%
31	10	2.2%
Other values (66)	235	52.7%
(Missing)	91	20.4%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0.42	1	0.2%
0.75	1	0.2%
1	3	0.7%
2	6	1.3%
3	4	0.9%
4	3	0.7%
5	3	0.7%
6	1	0.2%
7	1	0.2%
8	3	0.7%

Value	Count	Frequency (%)
0.42	1	0.2%
0.67	1	0.2%
0.75	2	0.4%
0.83	2	0.4%
0.92	1	0.2%
1	2	0.4%
2	6	1.3%
3	3	0.7%
4	3	0.7%
5	3	0.7%

Value	Count	Frequency (%)
0.42	1	0.2%
0.67	1	0.2%
0.75	2	0.4%
0.83	2	0.4%
0.92	1	0.2%
1	2	0.4%
2	6	1.3%
3	3	0.7%
4	3	0.7%
5	3	0.7%

Value	Count	Frequency (%)
0.42	1	0.2%
0.75	1	0.2%
1	3	0.7%
2	6	1.3%
3	4	0.9%
4	3	0.7%
5	3	0.7%
6	1	0.2%
7	1	0.2%
8	3	0.7%

SibSp
Real number (ℝ)

	Dataset A	Dataset B
Distinct	7	7
Distinct (%)	1.6%	1.6%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	0.5426009	0.56278027

	Dataset A	Dataset B
Minimum	0	0
Maximum	8	8
Zeros	297	302
Zeros (%)	66.6%	67.7%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0	0
5-th percentile	0	0
Q1	0	0
median	0	0
Q3	1	1
95-th percentile	2	2.75
Maximum	8	8
Range	8	8
Interquartile range (IQR)	1	1

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	1.1224878	1.2102021
Coefficient of variation (CV)	2.0687172	2.1503989
Kurtosis	18.535871	16.771978
Mean	0.5426009	0.56278027
Median Absolute Deviation (MAD)	0	0
Skewness	3.7667746	3.6922183
Sum	242	251
Variance	1.2599788	1.4645891
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
0	297	66.6%
1	111	24.9%
2	17	3.8%
4	8	1.8%
3	6	1.3%
8	4	0.9%
5	3	0.7%

Value	Count	Frequency (%)
0	302	67.7%
1	103	23.1%
2	18	4.0%
4	8	1.8%
3	5	1.1%
5	5	1.1%
8	5	1.1%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	297	66.6%
1	111	24.9%
2	17	3.8%
3	6	1.3%
4	8	1.8%
5	3	0.7%
8	4	0.9%

Value	Count	Frequency (%)
0	302	67.7%
1	103	23.1%
2	18	4.0%
3	5	1.1%
4	8	1.8%
5	5	1.1%
8	5	1.1%

Value	Count	Frequency (%)
0	302	67.7%
1	103	23.1%
2	18	4.0%
3	5	1.1%
4	8	1.8%
5	5	1.1%
8	5	1.1%

Value	Count	Frequency (%)
0	297	66.6%
1	111	24.9%
2	17	3.8%
3	6	1.3%
4	8	1.8%
5	3	0.7%
8	4	0.9%

Parch
Real number (ℝ)

	Dataset A	Dataset B
Distinct	7	7
Distinct (%)	1.6%	1.6%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	0.367713	0.39013453

	Dataset A	Dataset B
Minimum	0	0
Maximum	6	6
Zeros	345	338
Zeros (%)	77.4%	75.8%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0	0
5-th percentile	0	0
Q1	0	0
median	0	0
Q3	0	0
95-th percentile	2	2
Maximum	6	6
Range	6	6
Interquartile range (IQR)	0	0

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	0.82611187	0.82939885
Coefficient of variation (CV)	2.2466213	2.1259304
Kurtosis	12.319631	11.125881
Mean	0.367713	0.39013453
Median Absolute Deviation (MAD)	0	0
Skewness	3.0941863	2.8930729
Sum	164	174
Variance	0.68246083	0.68790245
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
0	345	77.4%
1	58	13.0%
2	34	7.6%
5	3	0.7%
3	3	0.7%
4	2	0.4%
6	1	0.2%

Value	Count	Frequency (%)
0	338	75.8%
1	60	13.5%
2	40	9.0%
3	3	0.7%
5	3	0.7%
6	1	0.2%
4	1	0.2%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	345	77.4%
1	58	13.0%
2	34	7.6%
3	3	0.7%
4	2	0.4%
5	3	0.7%
6	1	0.2%

Value	Count	Frequency (%)
0	338	75.8%
1	60	13.5%
2	40	9.0%
3	3	0.7%
4	1	0.2%
5	3	0.7%
6	1	0.2%

Value	Count	Frequency (%)
0	338	75.8%
1	60	13.5%
2	40	9.0%
3	3	0.7%
4	1	0.2%
5	3	0.7%
6	1	0.2%

Value	Count	Frequency (%)
0	345	77.4%
1	58	13.0%
2	34	7.6%
3	3	0.7%
4	2	0.4%
5	3	0.7%
6	1	0.2%

Ticket
['Text', 'Text']

	Dataset A	Dataset B
Distinct	378	380
Distinct (%)	84.8%	85.2%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Length

	Dataset A	Dataset B
Max length	18	18
Median length	17	17
Mean length	6.5650224	6.6547085
Min length	3	3

Characters and Unicode

	Dataset A	Dataset B
Total characters	2928	2968
Distinct characters	34	32
Distinct categories	5	5 ?
Distinct scripts	2	2 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	326	339 ?
Unique (%)	73.1%	76.0%

Sample

	Dataset A	Dataset B
1st row	SOTON/OQ 3101316	113781
2nd row	W./C. 6609	349248
3rd row	347082	2678
4th row	C.A. 2315	3101296
5th row	7598	334912

Value	Count	Frequency (%)
pc	24	4.3%
c.a	15	2.7%
ca	9	1.6%
ston/o	7	1.3%
2	7	1.3%
a/5	7	1.3%
2144	4	0.7%
a/4	4	0.7%
w./c	4	0.7%
line	4	0.7%
Other values (395)	468	84.6%

Value	Count	Frequency (%)
pc	32	5.7%
a/5	12	2.1%
ca	11	1.9%
c.a	9	1.6%
w./c	7	1.2%
2144	6	1.1%
382652	5	0.9%
sc/paris	5	0.9%
2343	5	0.9%
347082	5	0.9%
Other values (401)	468	82.8%

Most occurring characters

Value	Count	Frequency (%)
3	381	13.0%
1	337	11.5%
2	297	10.1%
4	248	8.5%
7	239	8.2%
6	206	7.0%
0	201	6.9%
5	191	6.5%
9	165	5.6%
8	138	4.7%
Other values (24)	525	17.9%

Value	Count	Frequency (%)
3	355	12.0%
1	344	11.6%
2	304	10.2%
4	251	8.5%
7	249	8.4%
6	210	7.1%
0	191	6.4%
5	184	6.2%
9	161	5.4%
8	145	4.9%
Other values (22)	574	19.3%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	2403	82.1%
Uppercase Letter	278	9.5%
Other Punctuation	128	4.4%
Space Separator	107	3.7%
Lowercase Letter	12	0.4%

Value	Count	Frequency (%)
Decimal Number	2394	80.7%
Uppercase Letter	300	10.1%
Other Punctuation	146	4.9%
Space Separator	119	4.0%
Lowercase Letter	9	0.3%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
3	381	15.9%
1	337	14.0%
2	297	12.4%
4	248	10.3%
7	239	9.9%
6	206	8.6%
0	201	8.4%
5	191	7.9%
9	165	6.9%
8	138	5.7%

Value	Count	Frequency (%)
3	355	14.8%
1	344	14.4%
2	304	12.7%
4	251	10.5%
7	249	10.4%
6	210	8.8%
0	191	8.0%
5	184	7.7%
9	161	6.7%
8	145	6.1%

Space Separator

Value	Count	Frequency (%)
	107	100.0%

Value	Count	Frequency (%)
	119	100.0%

Other Punctuation

Value	Count	Frequency (%)
.	87	68.0%
/	41	32.0%

Value	Count	Frequency (%)
.	98	67.1%
/	48	32.9%

Uppercase Letter

Value	Count	Frequency (%)
C	64	23.0%
O	43	15.5%
A	40	14.4%
P	40	14.4%
S	28	10.1%
N	20	7.2%
T	16	5.8%
W	6	2.2%
Q	5	1.8%
E	4	1.4%
Other values (5)	12	4.3%

Value	Count	Frequency (%)
C	84	28.0%
P	46	15.3%
A	44	14.7%
O	36	12.0%
S	32	10.7%
N	12	4.0%
T	11	3.7%
W	11	3.7%
F	6	2.0%
Q	5	1.7%
Other values (5)	13	4.3%

Lowercase Letter

Value	Count	Frequency (%)
a	3	25.0%
s	3	25.0%
r	2	16.7%
i	2	16.7%
l	1	8.3%
e	1	8.3%

Value	Count	Frequency (%)
a	3	33.3%
i	2	22.2%
s	2	22.2%
r	2	22.2%

Most occurring scripts

Value	Count	Frequency (%)
Common	2638	90.1%
Latin	290	9.9%

Value	Count	Frequency (%)
Common	2659	89.6%
Latin	309	10.4%

Most frequent character per script

Common

Value	Count	Frequency (%)
3	381	14.4%
1	337	12.8%
2	297	11.3%
4	248	9.4%
7	239	9.1%
6	206	7.8%
0	201	7.6%
5	191	7.2%
9	165	6.3%
8	138	5.2%
Other values (3)	235	8.9%

Value	Count	Frequency (%)
3	355	13.4%
1	344	12.9%
2	304	11.4%
4	251	9.4%
7	249	9.4%
6	210	7.9%
0	191	7.2%
5	184	6.9%
9	161	6.1%
8	145	5.5%
Other values (3)	265	10.0%

Latin

Value	Count	Frequency (%)
C	64	22.1%
O	43	14.8%
A	40	13.8%
P	40	13.8%
S	28	9.7%
N	20	6.9%
T	16	5.5%
W	6	2.1%
Q	5	1.7%
E	4	1.4%
Other values (11)	24	8.3%

Value	Count	Frequency (%)
C	84	27.2%
P	46	14.9%
A	44	14.2%
O	36	11.7%
S	32	10.4%
N	12	3.9%
T	11	3.6%
W	11	3.6%
F	6	1.9%
Q	5	1.6%
Other values (9)	22	7.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	2928	100.0%

Value	Count	Frequency (%)
ASCII	2968	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
3	381	13.0%
1	337	11.5%
2	297	10.1%
4	248	8.5%
7	239	8.2%
6	206	7.0%
0	201	6.9%
5	191	6.5%
9	165	5.6%
8	138	4.7%
Other values (24)	525	17.9%

Value	Count	Frequency (%)
3	355	12.0%
1	344	11.6%
2	304	10.2%
4	251	8.5%
7	249	8.4%
6	210	7.1%
0	191	6.4%
5	184	6.2%
9	161	5.4%
8	145	4.9%
Other values (22)	574	19.3%

Fare
Real number (ℝ)

	Dataset A	Dataset B
Distinct	170	184
Distinct (%)	38.1%	41.3%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	28.683744	33.485911

	Dataset A	Dataset B
Minimum	0	0
Maximum	512.3292	512.3292
Zeros	12	3
Zeros (%)	2.7%	0.7%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0	0
5-th percentile	7.05105	7.2292
Q1	7.8958	7.9031
median	13	15.0479
Q3	29.125	31.359375
95-th percentile	90	113.275
Maximum	512.3292	512.3292
Range	512.3292	512.3292
Interquartile range (IQR)	21.2292	23.456275

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	42.669032	50.731497
Coefficient of variation (CV)	1.4875684	1.5150102
Kurtosis	43.49269	37.783069
Mean	28.683744	33.485911
Median Absolute Deviation (MAD)	5.7708	7.76665
Skewness	5.2552911	5.069095
Sum	12792.95	14934.716
Variance	1820.6463	2573.6848
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
7.8958	23	5.2%
8.05	20	4.5%
13	19	4.3%
7.75	17	3.8%
26	14	3.1%
10.5	12	2.7%
0	12	2.7%
7.925	11	2.5%
7.225	9	2.0%
7.2292	9	2.0%
Other values (160)	300	67.3%

Value	Count	Frequency (%)
7.8958	23	5.2%
13	23	5.2%
8.05	21	4.7%
7.75	20	4.5%
26	15	3.4%
10.5	14	3.1%
7.225	8	1.8%
7.25	8	1.8%
7.8542	8	1.8%
7.775	7	1.6%
Other values (174)	299	67.0%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	12	2.7%
4.0125	1	0.2%
6.2375	1	0.2%
6.45	1	0.2%
6.4958	1	0.2%
6.75	2	0.4%
6.975	1	0.2%
7.0458	1	0.2%
7.05	3	0.7%
7.0542	2	0.4%

Value	Count	Frequency (%)
0	3	0.7%
4.0125	1	0.2%
6.2375	1	0.2%
6.8583	1	0.2%
6.95	1	0.2%
7.05	3	0.7%
7.0542	1	0.2%
7.125	2	0.4%
7.225	8	1.8%
7.2292	6	1.3%

Value	Count	Frequency (%)
0	3	0.7%
4.0125	1	0.2%
6.2375	1	0.2%
6.8583	1	0.2%
6.95	1	0.2%
7.05	3	0.7%
7.0542	1	0.2%
7.125	2	0.4%
7.225	8	1.8%
7.2292	6	1.3%

Value	Count	Frequency (%)
0	12	2.7%
4.0125	1	0.2%
6.2375	1	0.2%
6.45	1	0.2%
6.4958	1	0.2%
6.75	2	0.4%
6.975	1	0.2%
7.0458	1	0.2%
7.05	3	0.7%
7.0542	2	0.4%

Cabin
['Text', 'Text']

	Dataset A	Dataset B
Distinct	78	85
Distinct (%)	82.1%	84.2%
Missing	351	345
Missing (%)	78.7%	77.4%
Memory size	7.0 KiB	7.0 KiB

Length

	Dataset A	Dataset B
Max length	15	15
Median length	3	3
Mean length	3.3473684	3.5445545
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	318	358
Distinct characters	18	18
Distinct categories	3	3 ?
Distinct scripts	2	2 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	64	72 ?
Unique (%)	67.4%	71.3%

Sample

	Dataset A	Dataset B
1st row	E63	C22 C26
2nd row	B20	B78
3rd row	C104	E58
4th row	E44	C126
5th row	B51 B53 B55	C148

Value	Count	Frequency (%)
g6	4	3.7%
f33	3	2.8%
e24	2	1.9%
e8	2	1.9%
c65	2	1.9%
c123	2	1.9%
c2	2	1.9%
c68	2	1.9%
d	2	1.9%
d20	2	1.9%
Other values (80)	84	78.5%

Value	Count	Frequency (%)
c22	3	2.6%
f33	3	2.6%
c26	3	2.6%
f	3	2.6%
e101	3	2.6%
e8	2	1.7%
d36	2	1.7%
c52	2	1.7%
c68	2	1.7%
g73	2	1.7%
Other values (84)	90	78.3%

Most occurring characters

Value	Count	Frequency (%)
C	33	10.4%
3	29	9.1%
6	29	9.1%
2	27	8.5%
B	25	7.9%
D	22	6.9%
5	21	6.6%
1	20	6.3%
8	17	5.3%
0	16	5.0%
Other values (8)	79	24.8%

Value	Count	Frequency (%)
C	40	11.2%
2	35	9.8%
1	32	8.9%
3	31	8.7%
6	28	7.8%
B	25	7.0%
8	23	6.4%
E	19	5.3%
4	18	5.0%
7	17	4.7%
Other values (8)	90	25.1%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	199	62.6%
Uppercase Letter	107	33.6%
Space Separator	12	3.8%

Value	Count	Frequency (%)
Decimal Number	229	64.0%
Uppercase Letter	115	32.1%
Space Separator	14	3.9%

Most frequent character per category

Uppercase Letter

Value	Count	Frequency (%)
C	33	30.8%
B	25	23.4%
D	22	20.6%
E	14	13.1%
F	7	6.5%
G	4	3.7%
A	2	1.9%

Value	Count	Frequency (%)
C	40	34.8%
B	25	21.7%
E	19	16.5%
D	17	14.8%
F	7	6.1%
G	4	3.5%
A	3	2.6%

Decimal Number

Value	Count	Frequency (%)
3	29	14.6%
6	29	14.6%
2	27	13.6%
5	21	10.6%
1	20	10.1%
8	17	8.5%
0	16	8.0%
7	15	7.5%
4	13	6.5%
9	12	6.0%

Value	Count	Frequency (%)
2	35	15.3%
1	32	14.0%
3	31	13.5%
6	28	12.2%
8	23	10.0%
4	18	7.9%
7	17	7.4%
5	17	7.4%
0	14	6.1%
9	14	6.1%

Space Separator

Value	Count	Frequency (%)
	12	100.0%

Value	Count	Frequency (%)
	14	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	211	66.4%
Latin	107	33.6%

Value	Count	Frequency (%)
Common	243	67.9%
Latin	115	32.1%

Most frequent character per script

Latin

Value	Count	Frequency (%)
C	33	30.8%
B	25	23.4%
D	22	20.6%
E	14	13.1%
F	7	6.5%
G	4	3.7%
A	2	1.9%

Value	Count	Frequency (%)
C	40	34.8%
B	25	21.7%
E	19	16.5%
D	17	14.8%
F	7	6.1%
G	4	3.5%
A	3	2.6%

Common

Value	Count	Frequency (%)
3	29	13.7%
6	29	13.7%
2	27	12.8%
5	21	10.0%
1	20	9.5%
8	17	8.1%
0	16	7.6%
7	15	7.1%
4	13	6.2%
9	12	5.7%

Value	Count	Frequency (%)
2	35	14.4%
1	32	13.2%
3	31	12.8%
6	28	11.5%
8	23	9.5%
4	18	7.4%
7	17	7.0%
5	17	7.0%
0	14	5.8%
	14	5.8%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	318	100.0%

Value	Count	Frequency (%)
ASCII	358	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
C	33	10.4%
3	29	9.1%
6	29	9.1%
2	27	8.5%
B	25	7.9%
D	22	6.9%
5	21	6.6%
1	20	6.3%
8	17	5.3%
0	16	5.0%
Other values (8)	79	24.8%

Value	Count	Frequency (%)
C	40	11.2%
2	35	9.8%
1	32	8.9%
3	31	8.7%
6	28	7.8%
B	25	7.0%
8	23	6.4%
E	19	5.3%
4	18	5.0%
7	17	4.7%
Other values (8)	90	25.1%

Embarked
Categorical

	Dataset A	Dataset B
Distinct	3	3
Distinct (%)	0.7%	0.7%
Missing	1	1
Missing (%)	0.2%	0.2%
Memory size	7.0 KiB	7.0 KiB

S	327
C	79
Q	39

S	319
C	81
Q	45

Length

	Dataset A	Dataset B
Max length	1	1
Median length	1	1
Mean length	1	1
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	445	445
Distinct characters	3	3
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	S	S
2nd row	S	S
3rd row	S	C
4th row	S	S
5th row	S	Q

Common Values

Value	Count	Frequency (%)
S	327	73.3%
C	79	17.7%
Q	39	8.7%
(Missing)	1	0.2%

Value	Count	Frequency (%)
S	319	71.5%
C	81	18.2%
Q	45	10.1%
(Missing)	1	0.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
s	327	73.5%
c	79	17.8%
q	39	8.8%

Value	Count	Frequency (%)
s	319	71.7%
c	81	18.2%
q	45	10.1%

Most occurring characters

Value	Count	Frequency (%)
S	327	73.5%
C	79	17.8%
Q	39	8.8%

Value	Count	Frequency (%)
S	319	71.7%
C	81	18.2%
Q	45	10.1%

Most occurring categories

Value	Count	Frequency (%)
Uppercase Letter	445	100.0%

Value	Count	Frequency (%)
Uppercase Letter	445	100.0%

Most frequent character per category

Uppercase Letter

Value	Count	Frequency (%)
S	327	73.5%
C	79	17.8%
Q	39	8.8%

Value	Count	Frequency (%)
S	319	71.7%
C	81	18.2%
Q	45	10.1%

Most occurring scripts

Value	Count	Frequency (%)
Latin	445	100.0%

Value	Count	Frequency (%)
Latin	445	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
S	327	73.5%
C	79	17.8%
Q	39	8.8%

Value	Count	Frequency (%)
S	319	71.7%
C	81	18.2%
Q	45	10.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	445	100.0%

Value	Count	Frequency (%)
ASCII	445	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
S	327	73.5%
C	79	17.8%
Q	39	8.8%

Value	Count	Frequency (%)
S	319	71.7%
C	81	18.2%
Q	45	10.1%

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset A

A simple visualization of nullity by column.

Dataset B

A simple visualization of nullity by column.

Dataset A

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Dataset B

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

First rows
Last rows

Dataset A

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
511	512	0	3	Webber, Mr. James	male	NaN	0	0	SOTON/OQ 3101316	8.0500	NaN	S
235	236	0	3	Harknett, Miss. Alice Phoebe	female	NaN	0	0	W./C. 6609	7.5500	NaN	S
542	543	0	3	Andersson, Miss. Sigrid Elisabeth	female	11.0	4	2	347082	31.2750	NaN	S
788	789	1	3	Dean, Master. Bertram Vere	male	1.0	1	2	C.A. 2315	20.5750	NaN	S
338	339	1	3	Dahl, Mr. Karl Edwart	male	45.0	0	0	7598	8.0500	NaN	S
112	113	0	3	Barton, Mr. David John	male	22.0	0	0	324669	8.0500	NaN	S
462	463	0	1	Gee, Mr. Arthur H	male	47.0	0	0	111320	38.5000	E63	S
159	160	0	3	Sage, Master. Thomas Henry	male	NaN	8	2	CA. 2343	69.5500	NaN	S
846	847	0	3	Sage, Mr. Douglas Bullen	male	NaN	8	2	CA. 2343	69.5500	NaN	S
493	494	0	1	Artagaveytia, Mr. Ramon	male	71.0	0	0	PC 17609	49.5042	NaN	C

Dataset B

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
498	499	0	1	Allison, Mrs. Hudson J C (Bessie Waldo Daniels)	female	25.0	1	2	113781	151.5500	C22 C26	S
870	871	0	3	Balkic, Mr. Cerin	male	26.0	0	0	349248	7.8958	NaN	S
852	853	0	3	Boulos, Miss. Nourelain	female	9.0	1	1	2678	15.2458	NaN	C
528	529	0	3	Salonen, Mr. Johan Werner	male	39.0	0	0	3101296	7.9250	NaN	S
629	630	0	3	O'Connell, Mr. Patrick D	male	NaN	0	0	334912	7.7333	NaN	Q
31	32	1	1	Spencer, Mrs. William Augustus (Marie Eugenie)	female	NaN	1	0	PC 17569	146.5208	B78	C
662	663	0	1	Colley, Mr. Edward Pomeroy	male	47.0	0	0	5727	25.5875	E58	S
828	829	1	3	McCormack, Mr. Thomas Joseph	male	NaN	0	0	367228	7.7500	NaN	Q
614	615	0	3	Brocklebank, Mr. William Alfred	male	35.0	0	0	364512	8.0500	NaN	S
526	527	1	2	Ridsdale, Miss. Lucy	female	50.0	0	0	W./C. 14258	10.5000	NaN	S

Dataset A

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
667	668	0	3	Rommetvedt, Mr. Knud Paust	male	NaN	0	0	312993	7.7750	NaN	S
306	307	1	1	Fleming, Miss. Margaret	female	NaN	0	0	17421	110.8833	NaN	C
176	177	0	3	Lefebre, Master. Henry Forbes	male	NaN	3	1	4133	25.4667	NaN	S
603	604	0	3	Torber, Mr. Ernst William	male	44.0	0	0	364511	8.0500	NaN	S
64	65	0	1	Stewart, Mr. Albert A	male	NaN	0	0	PC 17605	27.7208	NaN	C
139	140	0	1	Giglio, Mr. Victor	male	24.0	0	0	PC 17593	79.2000	B86	C
532	533	0	3	Elias, Mr. Joseph Jr	male	17.0	1	1	2690	7.2292	NaN	C
314	315	0	2	Hart, Mr. Benjamin	male	43.0	1	1	F.C.C. 13529	26.2500	NaN	S
344	345	0	2	Fox, Mr. Stanley Hubert	male	36.0	0	0	229236	13.0000	NaN	S
340	341	1	2	Navratil, Master. Edmond Roger	male	2.0	1	1	230080	26.0000	F2	S

Dataset B

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
55	56	1	1	Woolner, Mr. Hugh	male	NaN	0	0	19947	35.5000	C52	S
272	273	1	2	Mellinger, Mrs. (Elizabeth Anne Maidment)	female	41.0	0	1	250644	19.5000	NaN	S
817	818	0	2	Mallet, Mr. Albert	male	31.0	1	1	S.C./PARIS 2079	37.0042	NaN	C
490	491	0	3	Hagland, Mr. Konrad Mathias Reiersen	male	NaN	1	0	65304	19.9667	NaN	S
15	16	1	2	Hewlett, Mrs. (Mary D Kingcome)	female	55.0	0	0	248706	16.0000	NaN	S
109	110	1	3	Moran, Miss. Bertha	female	NaN	1	0	371110	24.1500	NaN	Q
138	139	0	3	Osen, Mr. Olaf Elon	male	16.0	0	0	7534	9.2167	NaN	S
569	570	1	3	Jonsson, Mr. Carl	male	32.0	0	0	350417	7.8542	NaN	S
677	678	1	3	Turja, Miss. Anna Sofia	female	18.0	0	0	4138	9.8417	NaN	S
753	754	0	3	Jonkoff, Mr. Lalio	male	23.0	0	0	349204	7.8958	NaN	S

Dataset A

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	# duplicates
Dataset does not contain duplicate rows.

Dataset B

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	# duplicates
Dataset does not contain duplicate rows.

Overview

Variables

Common Values

Length

Common Values (Plot)

Dataset A

Dataset B

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Common Values (Plot)

Dataset A

Dataset B

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Most occurring characters

Most occurring categories

Most frequent character per category

Space Separator

Lowercase Letter

Uppercase Letter

Other Punctuation

Open Punctuation

Close Punctuation

Dash Punctuation

Most occurring scripts

Most frequent character per script

Common

Latin

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Common Values (Plot)

Dataset A

Dataset B

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Most occurring scripts

Most frequent character per script

Latin

Most occurring blocks

Most frequent character per block

ASCII

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Space Separator

Other Punctuation

Uppercase Letter

Lowercase Letter

Most occurring scripts

Most frequent character per script

Common

Latin

Most occurring blocks

Most frequent character per block

ASCII

Most occurring characters